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A Novel Adaptive Feature Fusion Strategy for Image Retrieval.

Xiaojun Lu1, Libo Zhang1, Lei Niu1

  • 1College of Sciences, North Eastern University, Shenyang 110819, China.

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Summary
This summary is machine-generated.

This study introduces an adaptive multi-feature fusion method for content-based image retrieval. The novel approach uses information entropy and PageRank to select and weight features, significantly improving retrieval accuracy in big data scenarios.

Keywords:
feature fusionimage retrievalinformation entropypagerank

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Area of Science:

  • Computer Science
  • Information Retrieval
  • Artificial Intelligence

Background:

  • Efficient image retrieval is crucial in the big data era.
  • Single-feature retrieval systems have limitations.
  • Multi-feature fusion enhances image retrieval performance.

Purpose of the Study:

  • To propose an adaptive multi-feature fusion strategy for content-based image retrieval.
  • To improve retrieval accuracy and generalization by dynamically selecting and weighting features.

Main Methods:

  • Image feature extraction and initial similarity calculation using information entropy.
  • Automatic effective feature selection based on retrieval trust derived from single-feature precision.
  • Feature weight optimization using the PageRank algorithm.
  • Comprehensive similarity calculation for final retrieval results.

Main Results:

  • The proposed method achieved high top-10 retrieval precision: 99.55% (Corel1k), 88.02% (UC Merced Land-Use), and 88.28% (RSSCN7).
  • The mean average precision (mAP) on the Holidays dataset was 92.46%.
  • The adaptive fusion strategy outperformed single-feature and fixed-feature fusion methods.

Conclusions:

  • The adaptive multi-feature fusion strategy offers superior performance and generalization compared to single-feature methods.
  • Dynamic feature selection and fusion optimize retrieval effectiveness for each query.
  • The method effectively addresses the challenges of image retrieval in large datasets.